import numpy as np
import csv

def load_iris_data():
    iris_data = []
    with open("iris.csv") as csvfile:
        csv_reader = csv.reader(csvfile)
        next(csv_reader) 
        for row in csv_reader:
            iris_data.append(row)
    return iris_data

def calculate_stats(iris_data):
    setosa_data = {'Sepal.Length': [], 'Sepal.Width': [], 'Petal.Length': [], 'Petal.Width': []}
    versicolor_data = {'Sepal.Length': [], 'Sepal.Width': [], 'Petal.Length': [], 'Petal.Width': []}
    virginica_data = {'Sepal.Length': [], 'Sepal.Width': [], 'Petal.Length': [], 'Petal.Width': []}
    
    for row in iris_data:
        species = row[5]
        sepal_length = float(row[1])
        sepal_width = float(row[2])
        petal_length = float(row[3])
        petal_width = float(row[4])
        if species == 'setosa':
            setosa_data['Sepal.Length'].append(sepal_length)
            setosa_data['Sepal.Width'].append(sepal_width)
            setosa_data['Petal.Length'].append(petal_length)
            setosa_data['Petal.Width'].append(petal_width)
        elif species == 'versicolor':
            versicolor_data['Sepal.Length'].append(sepal_length)
            versicolor_data['Sepal.Width'].append(sepal_width)
            versicolor_data['Petal.Length'].append(petal_length)
            versicolor_data['Petal.Width'].append(petal_width)
        elif species == 'virginica':
            virginica_data['Sepal.Length'].append(sepal_length)
            virginica_data['Sepal.Width'].append(sepal_width)
            virginica_data['Petal.Length'].append(petal_length)
            virginica_data['Petal.Width'].append(petal_width)
    
    stats = {}
    for species, data in [('setosa', setosa_data), ('versicolor', versicolor_data), ('virginica', virginica_data)]:
        stats[species] = {
            'Sepal.Length': {'mean': np.mean(data['Sepal.Length']), 'std': np.std(data['Sepal.Length'])},
            'Sepal.Width': {'mean': np.mean(data['Sepal.Width']), 'std': np.std(data['Sepal.Width'])},
            'Petal.Length': {'mean': np.mean(data['Petal.Length']), 'std': np.std(data['Petal.Length'])},
            'Petal.Width': {'mean': np.mean(data['Petal.Width']), 'std': np.std(data['Petal.Width'])}
        }
    return stats

def predict_species(new_data, stats):
    sepal_length, sepal_width, petal_length, petal_width = map(float, new_data)
    min_diff = float('inf')
    predicted_species = None
    
    for species in stats:
        diff = 0
        diff += abs((sepal_length - stats[species]['Sepal.Length']['mean']) / stats[species]['Sepal.Length']['std'])
        diff += abs((sepal_width - stats[species]['Sepal.Width']['mean']) / stats[species]['Sepal.Width']['std'])
        diff += abs((petal_length - stats[species]['Petal.Length']['mean']) / stats[species]['Petal.Length']['std'])
        diff += abs((petal_width - stats[species]['Petal.Width']['mean']) / stats[species]['Petal.Width']['std'])
        
        if diff < min_diff:
            min_diff = diff
            predicted_species = species
    return predicted_species

iris_data = load_iris_data()
stats = calculate_stats(iris_data)
new_flower = input().strip().split()
species = predict_species(new_flower, stats)
print(species.lower())